Electronic Journal of Structural Engineering

Volume 26, Issue 3 of the Electronic Journal of Structural Engineering brings together a targeted collection of original research articles highlighting contemporary advancements in structural engineering, with a particular emphasis on composite and built-up structural systems, seismic performance, advanced numerical modeling, and data-driven material characterization.

A prominent theme throughout this issue is the behavior and optimization of structural columns under complex loading scenarios. The contributions feature investigations into enhancing composite action in circular concrete-filled steel tube (CFST) columns using internal spiral plates, alongside finite element analyses evaluating lateral impact effects on carbon steel battened built-up columns. Together, these studies offer valuable insights into improving the load-carrying capacity, structural integrity, and dynamic resilience of critical column systems.

The issue also addresses innovations in construction technology and seismic engineering through research focused on optimizing the seismic performance and construction workflows of prefabricated steel structures using Building Information Modeling (BIM). This work underscores the vital role of digital integration in enhancing structural safety and construction efficiency.

Complementing these physical and numerical structural assessments is a strong contribution to intelligent material analysis. The issue highlights a systematic benchmarking and SHAP interpretability study utilizing explainable machine learning to predict compressive stress in cement-based construction materials, illustrating the growing capability of interpretable AI in materials science.

Collectively, the articles presented in this issue showcase how the integration of innovative structural details, advanced finite element modeling, BIM technology, and machine learning can be harnessed to advance the design, safety, and performance of modern civil infrastructure.